knitr::opts_chunk$set(echo = TRUE)

rm(list = ls())

#cmdstanr::set_cmdstan_path(path = "C:/Users/kueng/.cmdstan/cmdstan-2.35.0")
#cmdstanr::set_cmdstan_path(path = "C:/Users/pascku/.cmdstan/cmdstan-2.36.0")

library(tidyverse)
library(R.utils)
library(wbCorr)
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)
library(see)
library(beepr)
library(DHARMa)
library(digest)



source(file.path('Functions', 'ReportModels.R'))
source(file.path('Functions', 'PrettyTables.R'))
source(file.path('Functions', 'ReportMeasures.R'))
source(file.path('Functions', 'PrepareData.R'))

report_function_hash <- digest::digest(summarize_brms)
system("shutdown /a")
## [1] 1116
# Set options for analysis
use_mi = FALSE
shutdown = FALSE
report_ordinal = FALSE
do_priorsense = FALSE
get_bayesfactor = TRUE
check_models = TRUE #

if (get_bayesfactor) {
  stats_to_report <- c('CI', 'SE', 'pd', 'ROPE', 'BF', 'Rhat', 'ESS')
} else {
  stats_to_report <- c('CI', 'SE', 'pd', 'ROPE', 'Rhat', 'ESS')
}

options(
  dplyr.print_max = 100, 
  brms.backend = 'cmdstan',
  brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
  brms.file_refit = 'on_change',
  #brms.file_refit = 'always',
  error = function() {
    beepr::beep(sound = 5)
    if (shutdown) {
      system("shutdown /s /t 180")
      quit(save = "no", status = 1)
    }
  }
  , es.use_symbols = TRUE
)


####################### Model parameters #######################

iterations = 12000 # 12'000 per chain to achieve 40'000
warmup = 2000 # 2000

# NO AR!!!
#corstr = 'ar'
#corstr = 'cosy_couple'
#corstr = 'cosy_couple:user'


################################################################

suffix = paste0('_final_with_plan_', as.character(iterations))
df <- openxlsx::read.xlsx(file.path('long.xlsx'))
df_original <- df

df_double <- prepare_data(df, recode_pushing = TRUE, use_mi = use_mi)[[1]]

Constructing scales Re-coding pusing reshaping data (4field) centering data within and between

summary(df_double$pushing)

Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s 0.0000 0.0000 0.0000 0.1635 0.0000 5.0000 241

Modelling

# For indistinguishable Dyads
model_rows_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'persuasion_self_cw', 
    'persuasion_partner_cw', 
    'pressure_self_cw', 
    'pressure_partner_cw', 
    'pushing_self_cw', 
    'pushing_partner_cw', 
    'day', 
    'plan_selfPlan',
    'plan_partnerPlan',
    'weartime_self_cw',
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'persuasion_self_cb',
    'persuasion_partner_cb',
    'pressure_self_cb',
    'pressure_partner_cb',
    'pushing_self_cb',
    'pushing_partner_cb',
    'weartime_self_cb'
  )


model_rows_fixed_ordinal <- c(
  model_rows_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rows_fixed[2:length(model_rows_fixed)]
)

model_rows_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(persuasion_self_cw)',
  'sd(persuasion_partner_cw)',
  'sd(pressure_self_cw)',
  'sd(pressure_partner_cw)',
  'sd(pushing_self_cw)',
  'sd(pushing_partner_cw)',
  # '-- CORRELATION STRUCTURE -- ', 
  'sigma'
)

model_rows_random_ordinal <- c(model_rows_random,'disc')
# For indistinguishable Dyads
model_rownames_fixed <- c(
    "Intercept", 
    # "-- WITHIN PERSON MAIN EFFECTS --", 
    "Daily individual's experienced persuasion",  
    "Daily partner's experienced persuasion", 
    "Daily individual's experienced pressure", 
    "Daily partner's experienced pressure", 
    "Daily individual's experienced pushing", 
    "Daily partner's experienced pushing", 
    "Day", 
    "Own Actionplan",
    'Partner Actionplan',
    "Daily weartime",
    
    # "-- BETWEEN PERSON MAIN EFFECTS",
    "Mean individual's experienced persuasion", 
    "Mean partner's experienced persuasion", 
    "Mean individual's experienced pressure", 
    "Mean partner's experienced pressure", 
    "Mean individual's experienced pushing", 
    "Mean partner's experienced pushing", 
    "Mean weartime"
  )


model_rownames_fixed_ordinal <- c(
  model_rownames_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rownames_fixed[2:length(model_rownames_fixed)]
)

model_rownames_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  "sd(Daily individual's experienced persuasion)", 
  "sd(Daily partner's experienced persuasion)", # OR partner received
  "sd(Daily individual's experienced pressure)", 
  "sd(Daily partner's experienced pressure)", 
  "sd(Daily individual's experienced pushing)", 
  "sd(Daily partner's experienced pushing)", 
  # '-- CORRELATION STRUCTURE -- ', 
  'sigma'
)

model_rownames_random_ordinal <- c(model_rownames_random,'disc')
rows_to_pack <- list(
  "Within-Person Effects" = c(2,11),
  "Between-Person Effects" = c(12,18),
  "Random Effects" = c(19, 25), 
  "Additional Parameters" = c(26,26)
  )


rows_to_pack_ordinal <- list(
  "Within-Person Effects" = c(2+5,11+5),
  "Between-Person Effects" = c(12+5,18+5),
  "Random Effects" = c(19+5, 25+5), 
  "Additional Parameters" = c(26+5,26+6)
  )

Self-Reported MVPA

range(df_double$pa_sub, na.rm = T) 
## [1]   0 720
hist(df_double$pa_sub, breaks = 40) 

hist(log(df_double$pa_sub+00000000001), breaks = 40)

Hurdle Lognormal Model

formula <- bf(
  pa_sub ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day + 
    
    # Random effects
    (1 + persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | dd | coupleID),
  
  hu = ~ persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day + 
    
    # Random effects
    (1 + persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | dd | coupleID)
  
  , decomp = 'QR'
  #, autocor = autocor_str
) 

prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 2)", class = "b", dpar = "hu")
  , brms::set_prior("normal(0, 50)", class = "Intercept") # for non-zero PA
  , brms::set_prior("normal(0.5, 2.5)", class = "Intercept", dpar = 'hu') # hurdle part
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)

#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = hurdle_lognormal()
#)

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

pa_sub <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::hurdle_lognormal(), 
  #family = brms::hurdle_negbinomial(), 
  #family = brms::hurdle_poisson(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 42,
  file = file.path("models_cache_brms", paste0("pa_sub_hu_lognormal", suffix))
  #, file_refit = 'always'
)
## Warning: Rows containing NAs were excluded from the model.
pa_sub_digest <- digest::digest(pa_sub)
if (check_models) {
  check_brms(pa_sub, log_pp_check = TRUE)
  DHARMa.check_brms.all(pa_sub, integer = TRUE, outliers_type = 'bootstrap')
}
## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF   VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 1.56 [1.51, 1.62]         1.25      0.64
##  persuasion_partner_cw 1.08 [1.06, 1.12]         1.04      0.92
##       pressure_self_cw 1.06 [1.04, 1.10]         1.03      0.94
##    pressure_partner_cw 1.06 [1.04, 1.10]         1.03      0.94
##        pushing_self_cw 1.06 [1.04, 1.10]         1.03      0.94
##     pushing_partner_cw 1.08 [1.05, 1.12]         1.04      0.93
##     persuasion_self_cb 1.09 [1.06, 1.12]         1.04      0.92
##  persuasion_partner_cb 4.70 [4.48, 4.93]         2.17      0.21
##       pressure_self_cb 4.60 [4.39, 4.83]         2.15      0.22
##    pressure_partner_cb 3.26 [3.12, 3.41]         1.81      0.31
##        pushing_self_cb 3.16 [3.02, 3.30]         1.78      0.32
##     pushing_partner_cb 2.81 [2.69, 2.93]         1.68      0.36
##              plan_self 2.73 [2.61, 2.85]         1.65      0.37
##           plan_partner 1.37 [1.33, 1.42]         1.17      0.73
##                    day 1.37 [1.32, 1.42]         1.17      0.73
##  Tolerance 95% CI
##      [0.62, 0.66]
##      [0.89, 0.94]
##      [0.91, 0.96]
##      [0.91, 0.96]
##      [0.91, 0.96]
##      [0.90, 0.95]
##      [0.89, 0.94]
##      [0.20, 0.22]
##      [0.21, 0.23]
##      [0.29, 0.32]
##      [0.30, 0.33]
##      [0.34, 0.37]
##      [0.35, 0.38]
##      [0.70, 0.75]
##      [0.71, 0.76]
## # Distribution of Model Family
## 
## Predicted Distribution of Residuals
## 
##  Distribution Probability
##        cauchy        100%
## 
## Predicted Distribution of Response
## 
##                Distribution Probability
##  neg. binomial (zero-infl.)         78%
##               beta-binomial         16%
##                   lognormal          3%
## 
## Divergences:
## 0 of 40000 iterations ended with a divergence.
## 
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
## 
## Energy:
## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 1 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 7, observations = 3736, p-value = 0.88
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.000267666 0.003211991
## sample estimates:
## outlier frequency (expected: 0.00164882226980728 ) 
##                                        0.001873662
if (do_priorsense) {
  priorsense_vars <- c(
      'Intercept',
      'b_persuasion_self_cw',
      'b_persuasion_partner_cw',
      'b_pressure_self_cw',
      'b_pressure_partner_cw',
      'b_pushing_self_cw',
      'b_pushing_partner_cw'
  )
  
  hurdle_priorsense_vars <- c(
    'Intercept_hu',
    'b_hu_persuasion_self_cw',
    'b_hu_persuasion_partner_cw',
    'b_hu_pressure_self_cw',
    'b_hu_pressure_partner_cw',
    'b_hu_pushing_self_cw',
    'b_hu_pushing_partner_cw'
  )
  
  gc()
  priorsense::powerscale_sensitivity(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_dens(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_ecdf(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_quantities(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
}
# rope range for continuous part of the model
rope_factor <- sd(log(pa_sub$data$pa_sub[pa_sub$data$pa_sub > 0]))
rope_range_continuous = c(-0.1 * rope_factor, 0.1 * rope_factor)

summary_pa_sub <- summarize_brms(
  pa_sub, 
  stats_to_report = stats_to_report,
  rope_range = rope_range_continuous,
  hu_rope_range = c(-0.18, 0.18),
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) 
## Sampling priors, please wait...
## Warning in summarize_brms(pa_sub, stats_to_report = stats_to_report, rope_range
## = rope_range_continuous, : Coefficients were exponentiated. Double check if
## this was intended.
# Print the updated dataframe
summary_pa_sub %>%
  print_df(rows_to_pack = rows_to_pack)
exp(Est.)_hu SE_hu 95% CI_hu pd_hu ROPE_hu inside ROPE_hu BF_hu BF_Evidence_hu Rhat_hu Bulk_ESS_hu Tail_ESS_hu exp(Est.)_nonzero SE_nonzero 95% CI_nonzero pd_nonzero ROPE_nonzero inside ROPE_nonzero BF_nonzero BF_Evidence_nonzero Rhat_nonzero Bulk_ESS_nonzero Tail_ESS_nonzero
Intercept 0.26*** 0.04 [ 0.19, 0.35] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.000 17636 25222 37.84*** 2.60 [33.00, 43.33] 1.000 [0.92, 1.08] 0.000 >100 Overwhelming Evidence 1.001 9733 17297
Within-Person Effects
Daily individual’s experienced persuasion 1.58*** 0.11 [ 1.39, 1.83] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.000 25310 25796 1.03 0.03 [ 0.98, 1.09] 0.868 [0.92, 1.08] 0.963 0.017 Very Strong Evidence for Null 1.000 13834 23907
Daily partner’s experienced persuasion 1.34*** 0.08 [ 1.19, 1.52] 1.000 [0.84, 1.20] 0.035 >100 Overwhelming Evidence 1.000 27878 24532 1.03 0.02 [ 0.99, 1.08] 0.928 [0.92, 1.08] 0.974 0.023 Very Strong Evidence for Null 1.000 19254 25751
Daily individual’s experienced pressure 0.96 0.15 [ 0.69, 1.30] 0.614 [0.84, 1.20] 0.739 0.079 Strong Evidence for Null 1.000 27614 23572 0.91 0.05 [ 0.82, 1.00] 0.972 [0.92, 1.08] 0.345 0.050 Strong Evidence for Null 1.000 31401 26663
Daily partner’s experienced pressure 1.49* 0.28 [ 1.05, 2.34] 0.986 [0.84, 1.20] 0.111 1.185 Weak Evidence 1.000 26732 19474 0.95 0.04 [ 0.86, 1.03] 0.892 [0.92, 1.08] 0.700 0.014 Very Strong Evidence for Null 1.000 30682 28039
Daily individual’s experienced pushing 0.95 0.14 [ 0.71, 1.30] 0.636 [0.84, 1.20] 0.740 0.081 Strong Evidence for Null 1.000 20946 22281 0.99 0.03 [ 0.92, 1.05] 0.640 [0.92, 1.08] 0.969 0.008 Very Strong Evidence for Null 1.000 26058 25658
Daily partner’s experienced pushing 1.29* 0.14 [ 1.05, 1.62] 0.993 [0.84, 1.20] 0.247 1.030 Weak Evidence 1.000 29406 25834 0.96 0.03 [ 0.90, 1.02] 0.916 [0.92, 1.08] 0.878 0.018 Very Strong Evidence for Null 1.000 27923 27480
Day 0.86 0.13 [ 0.64, 1.16] 0.834 [0.84, 1.20] 0.576 0.119 Moderate Evidence for Null 1.000 45401 29525 0.99 0.06 [ 0.88, 1.12] 0.577 [0.92, 1.08] 0.786 0.007 Very Strong Evidence for Null 1.000 39959 29261
Own Actionplan 9.45*** 0.96 [ 7.76, 11.57] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.000 40464 31867 1.32*** 0.06 [ 1.21, 1.45] 1.000 [0.92, 1.08] 0.000 >100 Overwhelming Evidence 1.000 43927 29958
Partner Actionplan 1.17 0.12 [ 0.96, 1.42] 0.940 [0.84, 1.20] 0.601 0.168 Moderate Evidence for Null 1.000 37878 31193 1.08 0.05 [ 0.99, 1.17] 0.956 [0.92, 1.08] 0.541 0.032 Strong Evidence for Null 1.000 37238 29764
Daily weartime NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Between-Person Effects
Mean individual’s experienced persuasion 1.49 0.54 [ 0.72, 3.06] 0.863 [0.84, 1.20] 0.216 0.331 Weak Evidence for Null 1.000 9680 17445 0.99 0.15 [ 0.74, 1.34] 0.514 [0.92, 1.08] 0.394 0.015 Very Strong Evidence for Null 1.000 7913 15602
Mean partner’s experienced persuasion 1.40 0.50 [ 0.69, 2.85] 0.826 [0.84, 1.20] 0.252 0.280 Moderate Evidence for Null 1.000 9860 17614 0.95 0.14 [ 0.71, 1.28] 0.636 [0.92, 1.08] 0.371 0.015 Very Strong Evidence for Null 1.000 7546 15092
Mean individual’s experienced pressure 0.39* 0.16 [ 0.17, 0.88] 0.988 [0.84, 1.20] 0.030 2.722 Weak Evidence 1.000 14859 22272 1.21 0.21 [ 0.86, 1.69] 0.861 [0.92, 1.08] 0.203 0.019 Very Strong Evidence for Null 1.000 12502 22414
Mean partner’s experienced pressure 0.49 0.21 [ 0.21, 1.14] 0.951 [0.84, 1.20] 0.087 0.844 Weak Evidence for Null 1.000 15616 22856 0.94 0.17 [ 0.66, 1.33] 0.638 [0.92, 1.08] 0.318 0.011 Very Strong Evidence for Null 1.000 10957 20414
Mean individual’s experienced pushing 1.10 0.59 [ 0.38, 3.17] 0.573 [0.84, 1.20] 0.261 0.275 Moderate Evidence for Null 1.000 13848 21535 1.24 0.28 [ 0.80, 1.94] 0.838 [0.92, 1.08] 0.174 0.020 Very Strong Evidence for Null 1.001 9386 17470
Mean partner’s experienced pushing 1.96 1.04 [ 0.69, 5.73] 0.899 [0.84, 1.20] 0.120 0.591 Weak Evidence for Null 1.000 13955 21360 1.34 0.31 [ 0.85, 2.11] 0.903 [0.92, 1.08] 0.118 0.031 Strong Evidence for Null 1.000 8966 16483
Mean weartime NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Random Effects
sd(Intercept) 0.74 0.10 [0.57, 0.99] NA NA NA NA NA 1.000 16046 21340 0.30 0.04 [0.23, 0.40] NA NA NA NA NA 1.000 9362 16350
sd(Daily individual’s experienced persuasion) 0.21 0.09 [0.03, 0.39] NA NA NA NA NA 1.001 10442 7621 0.11 0.02 [0.07, 0.17] NA NA NA NA NA 1.000 17487 25737
sd(Daily partner’s experienced persuasion) 0.15 0.09 [0.01, 0.34] NA NA NA NA NA 1.000 9486 10913 0.08 0.02 [0.04, 0.13] NA NA NA NA NA 1.000 11687 7419
sd(Daily individual’s experienced pressure) 0.20 0.18 [0.01, 0.74] NA NA NA NA NA 1.000 15688 17682 0.07 0.06 [0.00, 0.23] NA NA NA NA NA 1.000 13071 17615
sd(Daily partner’s experienced pressure) 0.24 0.22 [0.01, 0.94] NA NA NA NA NA 1.000 14003 16516 0.06 0.05 [0.00, 0.18] NA NA NA NA NA 1.000 16359 14468
sd(Daily individual’s experienced pushing) 0.59 0.17 [0.31, 1.01] NA NA NA NA NA 1.000 16943 23757 0.09 0.04 [0.01, 0.17] NA NA NA NA NA 1.000 9282 7265
sd(Daily partner’s experienced pushing) 0.18 0.14 [0.01, 0.50] NA NA NA NA NA 1.000 14479 13486 0.07 0.04 [0.01, 0.15] NA NA NA NA NA 1.000 11065 10435
Additional Parameters
sigma NA NA NA NA NA NA NA NA NA NA NA 0.68 0.01 [0.65, 0.70] NA NA NA NA NA 1.000 42655 29595 
# Plot continuous part of model

variable <- c(
  '(Intercept)',
  'b_persuasion_self_cw',
  'b_persuasion_partner_cw',
  'b_pressure_self_cw',
  'b_pressure_partner_cw',
  'b_pushing_self_cw',
  'b_pushing_partner_cw'
)


plot(
  bayestestR::p_direction(pa_sub, parameter = variable),
  priors = TRUE
) + theme_bw()
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(
    pa_sub, 
    parameter = variable, 
    range = rope_range_continuous,
    verbose = F,
    ci = 1
  )
) + theme_bw()

# Hurdle part of the model
variable <- c(
  'b_hu_persuasion_self_cw',
  'b_hu_persuasion_partner_cw',
  'b_hu_pressure_self_cw',
  'b_hu_pressure_partner_cw',
  'b_hu_pushing_self_cw',
  'b_hu_pushing_partner_cw'
)

plot(
  bayestestR::p_direction(pa_sub, parameter = variable),
  priors = TRUE
) + theme_bw()
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length

# The rope range for the bernoulli part of the model is -0.18, 0.18
plot(
  bayestestR::rope(pa_sub, parameter = variable, range = c(-0.18, 0.18), ci = 1),
  verbose = FALSE
) + theme_bw()
## Possible multicollinearity between b_hu_persuasion_partner_cb and
##   b_hu_persuasion_self_cb (r = 0.75), b_persuasion_partner_cb and
##   b_persuasion_self_cb (r = 0.75). This might lead to inappropriate
##   results. See 'Details' in '?rope'.

Hurdle part of the model on the left, non-zero part towards the right side of the table

conds_eff <- conditional_spaghetti(
  pa_sub, 
  effects = c(
    'persuasion_self_cw',
    'persuasion_partner_cw',
    'pressure_self_cw',
    'pressure_partner_cw',
    'pushing_self_cw',
    'pushing_partner_cw'
  ),
  x_label = c(
    'Received Persuasion',
    'Exerted Persuasion',
    'Received Pressure',
    'Exerted Pressure',
    'Received Plan-Related Pushing',
    'Exerted Plan-Related Pushing'
  ),
  group_var = 'coupleID',
  plot_full_range = TRUE,
  y_limits = c(0, 100),
  y_label = "Same-Day MVPA",
  y_labels = c('Probability of Being Active', 'Minutes of MVPA When Active', 'Overall Expected Minutes of MVPA'),
  , filter_quantiles = .9995
  , font_family = 'Candara'
)
## This is posterior version 1.6.0
## 
## Attaching package: 'posterior'
## The following object is masked from 'package:bayesplot':
## 
##     rhat
## The following objects are masked from 'package:stats':
## 
##     mad, sd, var
## The following objects are masked from 'package:base':
## 
##     %in%, match
## Registering fonts with R
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
print(conds_eff)

$persuasion_self_cw

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning: Removed 73 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning: Removed 27 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.00809
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

$persuasion_partner_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning: Removed 51 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.0063
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

$pressure_self_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.0112
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

$pressure_partner_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Picking joint bandwidth of 0.0185
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

$pushing_self_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Picking joint bandwidth of 0.0106
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
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$pushing_partner_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Picking joint bandwidth of 0.00979
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Note. This graphic illustrates the relationship between social control and moderate to vigorous physical activity (MVPA) using a Bayesian Hurdle-Lognormal Multilevel Model. The predictor is centered within individuals to examine how deviations from their average social control relate to same-day MVPA. Shaded areas indicate credible intervals, thick lines show fixed effects, and thin lines represent random effects, highlighting variability across couples. The plots display the probability of being active, expected minutes of MVPA when active, and combined predicted MVPA. The bottom density plot visualizes the posterior distributions of slope estimates, transformed to represent multiplicative changes in odds ratios (hurdle component) or expected values. Medians and 95% credible intervals (2.5th and 97.5th percentiles) are shown. Effects are significant, when the 95% credible interval does not overlap 1.

x_label = c(
    'Received Persuasion',
    'Exerted Persuasion',
    'Received Pressure',
    'Exerted Pressure',
    'Received Plan-Related Pushing',
    'Exerted Plan-Related Pushing'
  )

home_dir <- getwd()
output_dir <- file.path(home_dir, 'Output', 'Plots')

for (i in 1:length(conds_eff)) {
  effname <- names(conds_eff)[i]
  eff_plot <- conds_eff[[i]]
  x_label_i <- x_label[[i]]
  
  rmarkdown::render(
    file.path(output_dir, 'BeautifulPlotWithNote.Rmd'), 
    output_file = file.path(output_dir, paste0('Graphic_', effname, '.pdf')),
    params = list(
      home_dir = home_dir,
      output_dir = output_dir,
      p_i = eff_plot,
      p_name = effname,
      x_label = x_label_i
      ),
    envir = new.env(),
    quiet = TRUE
  )
}

print('done')

Comparing effect size of pressure and pushing

hypothesis(pa_sub, "pressure_self_cw < pushing_self_cw")
## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (pressure_self_cw... < 0    -0.09      0.07    -0.19     0.02        9.8
##   Post.Prob Star
## 1      0.91     
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.

Device Based MVPA

range(df_double$pa_obj, na.rm = T) 
## [1]   5.75 971.25
hist(df_double$pa_obj, breaks = 50)

df_double$pa_obj_log <- log(df_double$pa_obj)

hist(df_double$pa_obj_log, breaks = 50)

Lognormal Model

formula <- bf(
  pa_obj ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day + weartime_self_cw + weartime_self_cb +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID)
  
  , decomp = 'QR'
  #, autocor = autocor_str
)



prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 50)", class = "Intercept") 
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = lognormal()
#  )

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

pa_obj_log <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = lognormal(),
  #control = list(adapt_delta = 0.95),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("pa_obj_log_gaussian", suffix))
)
## Warning: Rows containing NAs were excluded from the model.
pa_obj_log_digest <- digest::digest(pa_obj_log)
if (check_models) {
  check_brms(pa_obj_log, log_pp_check = TRUE)
  DHARMa.check_brms.all(pa_obj_log, integer = TRUE, outliers_type = 'bootstrap')
}
## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF    VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 1.11 [1.08,  1.16]         1.05      0.90
##  persuasion_partner_cw 1.12 [1.09,  1.17]         1.06      0.89
##       pressure_self_cw 1.08 [1.05,  1.12]         1.04      0.93
##    pressure_partner_cw 1.11 [1.07,  1.15]         1.05      0.90
##        pushing_self_cw 1.13 [1.10,  1.18]         1.06      0.88
##     pushing_partner_cw 1.18 [1.14,  1.23]         1.09      0.85
##       pressure_self_cb 4.03 [3.82,  4.25]         2.01      0.25
##    pressure_partner_cb 4.34 [4.12,  4.59]         2.08      0.23
##              plan_self 1.27 [1.22,  1.32]         1.13      0.79
##           plan_partner 1.28 [1.24,  1.34]         1.13      0.78
##                    day 1.04 [1.02,  1.09]         1.02      0.96
##       weartime_self_cw 1.04 [1.02,  1.09]         1.02      0.96
##       weartime_self_cb 1.26 [1.22,  1.31]         1.12      0.79
##  Tolerance 95% CI
##      [0.86, 0.93]
##      [0.86, 0.92]
##      [0.89, 0.95]
##      [0.87, 0.93]
##      [0.85, 0.91]
##      [0.81, 0.87]
##      [0.24, 0.26]
##      [0.22, 0.24]
##      [0.76, 0.82]
##      [0.75, 0.81]
##      [0.92, 0.98]
##      [0.92, 0.98]
##      [0.76, 0.82]
## 
## Moderate Correlation
## 
##                   Term  VIF    VIF 95% CI Increased SE Tolerance
##     persuasion_self_cb 9.10 [8.58,  9.64]         3.02      0.11
##  persuasion_partner_cb 9.75 [9.20, 10.33]         3.12      0.10
##        pushing_self_cb 6.24 [5.90,  6.61]         2.50      0.16
##     pushing_partner_cb 6.00 [5.68,  6.35]         2.45      0.17
##  Tolerance 95% CI
##      [0.10, 0.12]
##      [0.10, 0.11]
##      [0.15, 0.17]
##      [0.16, 0.18]
## # Distribution of Model Family
## 
## Predicted Distribution of Residuals
## 
##  Distribution Probability
##        cauchy         88%
##     lognormal          9%
##       weibull          3%
## 
## Predicted Distribution of Response
## 
##                Distribution Probability
##                   lognormal         72%
##                     tweedie         16%
##  neg. binomial (zero-infl.)          9%
## 
## Divergences:
## 0 of 40000 iterations ended with a divergence.
## 
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
## 
## Energy:
## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## DHARMa:testOutliers with type = binomial may have inflated Type I error rates for integer-valued distributions. To get a more exact result, it is recommended to re-run testOutliers with type = 'bootstrap'. See ?testOutliers for details

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 29, observations = 3337, p-value < 2.2e-16
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.0008990111 0.0047947258
## sample estimates:
## outlier frequency (expected: 0.00302067725501948 ) 
##                                        0.008690441
if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(pa_obj_log, variable = priorsense_vars)
}
# rope range for lognormal model
rope_factor <- sd(log(pa_obj_log$data$pa_obj))
rope_range_log = c(-0.1 * rope_factor, 0.1 * rope_factor)

summary_pa_obj <- summarize_brms(
  pa_obj_log, 
  stats_to_report = stats_to_report,
  rope_range = rope_range_log,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) 
## Sampling priors, please wait...
## Warning in summarize_brms(pa_obj_log, stats_to_report = stats_to_report, :
## Coefficients were exponentiated. Double check if this was intended.
summary_pa_obj %>%
  print_df(rows_to_pack = rows_to_pack)
exp(Est.) SE 95% CI pd ROPE inside ROPE BF BF_Evidence Rhat Bulk_ESS Tail_ESS
Intercept 111.19*** 6.10 [99.90, 123.80] 1.000 [0.94, 1.07] 0.000 >100 Overwhelming Evidence 1.000 7302 14610
Within-Person Effects
Daily individual’s experienced persuasion 1.03 0.02 [ 1.00, 1.06] 0.954 [0.94, 1.07] 0.995 0.023 Very Strong Evidence for Null 1.000 31745 31489
Daily partner’s experienced persuasion 1.02 0.02 [ 0.99, 1.05] 0.866 [0.94, 1.07] 0.998 0.010 Very Strong Evidence for Null 1.000 36811 32211
Daily individual’s experienced pressure 0.95 0.03 [ 0.88, 1.01] 0.950 [0.94, 1.07] 0.599 0.019 Very Strong Evidence for Null 1.000 58948 32857
Daily partner’s experienced pressure 0.98 0.03 [ 0.92, 1.05] 0.692 [0.94, 1.07] 0.911 0.005 Very Strong Evidence for Null 1.000 62674 30987
Daily individual’s experienced pushing 1.01 0.02 [ 0.96, 1.07] 0.730 [0.94, 1.07] 0.976 0.007 Very Strong Evidence for Null 1.000 43490 31579
Daily partner’s experienced pushing 1.00 0.02 [ 0.96, 1.04] 0.517 [0.94, 1.07] 0.997 0.005 Very Strong Evidence for Null 1.000 59076 31365
Day 0.97 0.03 [ 0.91, 1.04] 0.786 [0.94, 1.07] 0.842 0.006 Very Strong Evidence for Null 1.000 86932 31131
Own Actionplan 1.06* 0.03 [ 1.01, 1.12] 0.994 [0.94, 1.07] 0.527 0.103 Moderate Evidence for Null 1.000 84768 29763
Partner Actionplan 1.05 0.03 [ 1.00, 1.10] 0.972 [0.94, 1.07] 0.751 0.025 Very Strong Evidence for Null 1.000 80571 29234
Daily weartime 1.00*** 0.00 [ 1.00, 1.00] 1.000 [0.94, 1.07] 1.000 15.446 Strong Evidence 1.000 91257 29645
Between-Person Effects
Mean individual’s experienced persuasion 1.10 0.15 [ 0.84, 1.46] 0.761 [0.94, 1.07] 0.281 0.016 Very Strong Evidence for Null 1.001 8040 15329
Mean partner’s experienced persuasion 0.98 0.14 [ 0.74, 1.30] 0.560 [0.94, 1.07] 0.352 0.013 Very Strong Evidence for Null 1.001 8085 14898
Mean individual’s experienced pressure 0.99 0.14 [ 0.74, 1.32] 0.530 [0.94, 1.07] 0.345 0.007 Very Strong Evidence for Null 1.001 10570 20107
Mean partner’s experienced pressure 0.97 0.14 [ 0.74, 1.29] 0.577 [0.94, 1.07] 0.352 0.008 Very Strong Evidence for Null 1.000 9726 16655
Mean individual’s experienced pushing 0.95 0.19 [ 0.64, 1.44] 0.600 [0.94, 1.07] 0.246 0.011 Very Strong Evidence for Null 1.000 9570 17205
Mean partner’s experienced pushing 1.22 0.24 [ 0.82, 1.83] 0.842 [0.94, 1.07] 0.154 0.018 Very Strong Evidence for Null 1.000 9223 16339
Mean weartime 1.00 0.00 [ 1.00, 1.00] 0.916 [0.94, 1.07] 1.000 0.017 Very Strong Evidence for Null 1.000 43760 32719
Random Effects
sd(Intercept) 0.29 0.04 [0.23, 0.39] NA NA NA NA NA 1.000 9588 17646
sd(Daily individual’s experienced persuasion) 0.05 0.01 [0.02, 0.08] NA NA NA NA NA 1.000 21159 18046
sd(Daily partner’s experienced persuasion) 0.05 0.02 [0.02, 0.09] NA NA NA NA NA 1.000 17608 14641
sd(Daily individual’s experienced pressure) 0.04 0.03 [0.00, 0.14] NA NA NA NA NA 1.000 22275 23013
sd(Daily partner’s experienced pressure) 0.03 0.03 [0.00, 0.11] NA NA NA NA NA 1.000 24379 20553
sd(Daily individual’s experienced pushing) 0.07 0.04 [0.01, 0.15] NA NA NA NA NA 1.000 10463 14026
sd(Daily partner’s experienced pushing) 0.03 0.03 [0.00, 0.10] NA NA NA NA NA 1.000 15655 21319
Additional Parameters
sigma 0.57 0.01 [0.56, 0.59] NA NA NA NA NA 1.001 74585 28663 
plot(
  bayestestR::p_direction(pa_obj_log),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-3, 3)) +
  theme_bw()
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(pa_obj_log, range = rope_range_log, ci = 1)
) + theme_bw()
## Possible multicollinearity between b_persuasion_partner_cb and
##   b_persuasion_self_cb (r = 0.89), b_pressure_self_cb and
##   b_persuasion_self_cb (r = 0.73), b_pressure_partner_cb and
##   b_persuasion_self_cb (r = 0.74), b_pressure_self_cb and
##   b_persuasion_partner_cb (r = 0.71), b_pressure_partner_cb and
##   b_persuasion_partner_cb (r = 0.77), b_pushing_partner_cb and
##   b_pushing_self_cb (r = 0.83). This might lead to inappropriate results.
##   See 'Details' in '?rope'.

# Nothing significant, no plots

Affect

range(df_double$aff, na.rm = T)
## [1] 0 5
hist(df_double$aff, breaks = 15)

Gaussian

formula <- bf(
  aff ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID)
  
  , decomp = 'QR'
  #, autocor = autocor_str
)


prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b")
  ,brms::set_prior("normal(0, 20)", class = "Intercept", lb=1, ub=6)
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)

#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = gaussian()
#  )

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

mood_gauss <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("mood_gauss", suffix))
)
## Warning: Rows containing NAs were excluded from the model.
mood_gauss_digest <- digest::digest(mood_gauss)
if (check_models) {
  check_brms(mood_gauss, log_pp_check = FALSE)
  DHARMa.check_brms.all(mood_gauss, integer = FALSE)
}
## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF     VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 1.12 [ 1.09,  1.17]         1.06      0.89
##  persuasion_partner_cw 1.08 [ 1.05,  1.12]         1.04      0.93
##       pressure_self_cw 1.09 [ 1.07,  1.14]         1.05      0.91
##    pressure_partner_cw 1.08 [ 1.05,  1.12]         1.04      0.93
##        pushing_self_cw 1.16 [ 1.12,  1.20]         1.08      0.86
##     pushing_partner_cw 1.15 [ 1.11,  1.19]         1.07      0.87
##              plan_self 1.28 [ 1.24,  1.33]         1.13      0.78
##           plan_partner 1.27 [ 1.23,  1.32]         1.13      0.79
##                    day 1.05 [ 1.02,  1.09]         1.02      0.96
##  Tolerance 95% CI
##      [0.86, 0.92]
##      [0.89, 0.95]
##      [0.88, 0.94]
##      [0.89, 0.95]
##      [0.83, 0.89]
##      [0.84, 0.90]
##      [0.75, 0.81]
##      [0.76, 0.81]
##      [0.92, 0.98]
## 
## Moderate Correlation
## 
##                 Term  VIF     VIF 95% CI Increased SE Tolerance
##     pressure_self_cb 5.89 [ 5.58,  6.22]         2.43      0.17
##  pressure_partner_cb 5.90 [ 5.59,  6.22]         2.43      0.17
##      pushing_self_cb 8.80 [ 8.33,  9.30]         2.97      0.11
##   pushing_partner_cb 8.82 [ 8.34,  9.32]         2.97      0.11
##  Tolerance 95% CI
##      [0.16, 0.18]
##      [0.16, 0.18]
##      [0.11, 0.12]
##      [0.11, 0.12]
## 
## High Correlation
## 
##                   Term   VIF     VIF 95% CI Increased SE Tolerance
##     persuasion_self_cb 14.99 [14.16, 15.87]         3.87      0.07
##  persuasion_partner_cb 14.88 [14.06, 15.75]         3.86      0.07
##  Tolerance 95% CI
##      [0.06, 0.07]
##      [0.06, 0.07]
## # Distribution of Model Family
## 
## Predicted Distribution of Residuals
## 
##  Distribution Probability
##        cauchy         44%
##        normal         41%
##   exponential          6%
## 
## Predicted Distribution of Response
## 
##   Distribution Probability
##        tweedie         41%
##  beta-binomial         38%
##    half-cauchy          6%
## 
## Divergences:
## 0 of 40000 iterations ended with a divergence.
## 
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
## 
## Energy:
## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  model.check
## outliers at both margin(s) = 29, observations = 3736, p-value =
## 1.68e-09
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
##  0.005204527 0.011129091
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 ) 
##                                          0.007762313
if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(mood_gauss, variable = priorsense_vars)
}
summary_mood <- summarize_brms(
  mood_gauss, 
  stats_to_report = stats_to_report,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) 
## Warning in compute_rope(range = rope_range): Collinearity detected. Some VIFs
## are > 10. This may invalidate ROPE inferences!

Check for Multicollinearity

Low Correlation

              Term  VIF     VIF 95% CI Increased SE Tolerance
persuasion_self_cw 1.12 [ 1.09,  1.17]         1.06      0.89

persuasion_partner_cw 1.08 [ 1.05, 1.12] 1.04 0.93 pressure_self_cw 1.09 [ 1.07, 1.14] 1.05 0.91 pressure_partner_cw 1.08 [ 1.05, 1.12] 1.04 0.93 pushing_self_cw 1.16 [ 1.12, 1.20] 1.08 0.86 pushing_partner_cw 1.15 [ 1.11, 1.19] 1.07 0.87 plan_self 1.28 [ 1.24, 1.33] 1.13 0.78 plan_partner 1.27 [ 1.23, 1.32] 1.13 0.79 day 1.05 [ 1.02, 1.09] 1.02 0.96 Tolerance 95% CI [0.86, 0.92] [0.89, 0.95] [0.88, 0.94] [0.89, 0.95] [0.83, 0.89] [0.84, 0.90] [0.75, 0.81] [0.76, 0.81] [0.92, 0.98]

Moderate Correlation

            Term  VIF     VIF 95% CI Increased SE Tolerance
pressure_self_cb 5.89 [ 5.58,  6.22]         2.43      0.17

pressure_partner_cb 5.90 [ 5.59, 6.22] 2.43 0.17 pushing_self_cb 8.80 [ 8.33, 9.30] 2.97 0.11 pushing_partner_cb 8.82 [ 8.34, 9.32] 2.97 0.11 Tolerance 95% CI [0.16, 0.18] [0.16, 0.18] [0.11, 0.12] [0.11, 0.12]

High Correlation

              Term   VIF     VIF 95% CI Increased SE Tolerance
persuasion_self_cb 14.99 [14.16, 15.87]         3.87      0.07

persuasion_partner_cb 14.88 [14.06, 15.75] 3.86 0.07 Tolerance 95% CI [0.06, 0.07] [0.06, 0.07]

## Sampling priors, please wait...
summary_mood %>%
  print_df(rows_to_pack = rows_to_pack)
Est. SE 95% CI pd ROPE inside ROPE BF BF_Evidence Rhat Bulk_ESS Tail_ESS
Intercept 3.67*** 0.11 [ 3.46, 3.88] 1.000 [-0.11, 0.11] 0.000 >100 Overwhelming Evidence 1.000 4390 8992
Within-Person Effects
Daily individual’s experienced persuasion 0.00 0.02 [-0.04, 0.04] 0.521 [-0.11, 0.11] 1.000 0.004 Very Strong Evidence for Null 1.000 39834 29740
Daily partner’s experienced persuasion 0.02 0.02 [-0.02, 0.07] 0.813 [-0.11, 0.11] 1.000 0.006 Very Strong Evidence for Null 1.000 31489 29249
Daily individual’s experienced pressure -0.03 0.05 [-0.14, 0.07] 0.716 [-0.11, 0.11] 0.936 0.004 Very Strong Evidence for Null 1.000 44219 28749
Daily partner’s experienced pressure -0.03 0.05 [-0.15, 0.08] 0.691 [-0.11, 0.11] 0.927 0.004 Very Strong Evidence for Null 1.000 37429 26049
Daily individual’s experienced pushing 0.01 0.03 [-0.06, 0.07] 0.575 [-0.11, 0.11] 0.999 0.004 Very Strong Evidence for Null 1.000 43555 30803
Daily partner’s experienced pushing 0.07* 0.03 [ 0.00, 0.14] 0.977 [-0.11, 0.11] 0.896 0.034 Strong Evidence for Null 1.000 33119 28321
Day 0.26*** 0.06 [ 0.15, 0.37] 1.000 [-0.11, 0.11] 0.006 62.833 Very Strong Evidence 1.000 65116 29860
Own Actionplan 0.10** 0.04 [ 0.03, 0.18] 0.996 [-0.11, 0.11] 0.619 0.106 Moderate Evidence for Null 1.000 66702 29058
Partner Actionplan -0.03 0.04 [-0.11, 0.04] 0.791 [-0.11, 0.11] 0.983 0.004 Very Strong Evidence for Null 1.000 62577 30130
Daily weartime NA NA NA NA NA NA NA NA NA NA NA
Between-Person Effects
Mean individual’s experienced persuasion 0.34 0.28 [-0.23, 0.90] 0.887 [-0.11, 0.11] 0.150 0.028 Very Strong Evidence for Null 1.000 4803 9226
Mean partner’s experienced persuasion 0.23 0.28 [-0.34, 0.79] 0.791 [-0.11, 0.11] 0.231 0.019 Very Strong Evidence for Null 1.000 4707 9230
Mean individual’s experienced pressure -0.30 0.27 [-0.85, 0.25] 0.863 [-0.11, 0.11] 0.182 0.016 Very Strong Evidence for Null 1.000 5644 11725
Mean partner’s experienced pressure -0.32 0.27 [-0.87, 0.23] 0.876 [-0.11, 0.11] 0.172 0.017 Very Strong Evidence for Null 1.000 5485 11919
Mean individual’s experienced pushing 0.20 0.39 [-0.57, 0.99] 0.699 [-0.11, 0.11] 0.206 0.012 Very Strong Evidence for Null 1.002 6146 11770
Mean partner’s experienced pushing 0.36 0.39 [-0.41, 1.15] 0.827 [-0.11, 0.11] 0.150 0.017 Very Strong Evidence for Null 1.002 6107 11814
Mean weartime NA NA NA NA NA NA NA NA NA NA NA
Random Effects
sd(Intercept) 0.60 0.07 [0.47, 0.78] NA NA NA NA NA 1.001 7123 14047
sd(Daily individual’s experienced persuasion) 0.04 0.03 [0.00, 0.10] NA NA NA NA NA 1.000 10238 15606
sd(Daily partner’s experienced persuasion) 0.07 0.03 [0.01, 0.13] NA NA NA NA NA 1.000 9815 7528
sd(Daily individual’s experienced pressure) 0.07 0.06 [0.00, 0.23] NA NA NA NA NA 1.000 16476 18018
sd(Daily partner’s experienced pressure) 0.08 0.07 [0.00, 0.26] NA NA NA NA NA 1.000 14238 17562
sd(Daily individual’s experienced pushing) 0.05 0.04 [0.00, 0.14] NA NA NA NA NA 1.000 14637 15323
sd(Daily partner’s experienced pushing) 0.07 0.05 [0.01, 0.16] NA NA NA NA NA 1.000 13455 13980
Additional Parameters
sigma 0.96 0.01 [0.94, 0.98] NA NA NA NA NA 1.000 61626 30661 
plot(
  bayestestR::p_direction(mood_gauss),
  priors = TRUE
)  + 
  coord_cartesian(xlim = c(-3, 3)) +
  theme_bw()
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(mood_gauss, ci = 1)
) + theme_bw()
## Possible multicollinearity between b_pressure_self_cb and
##   b_persuasion_self_cb (r = 0.83), b_pressure_partner_cb and
##   b_persuasion_self_cb (r = 0.8), b_pressure_self_cb and
##   b_persuasion_partner_cb (r = 0.8), b_pressure_partner_cb and
##   b_persuasion_partner_cb (r = 0.83), b_pressure_partner_cb and
##   b_pressure_self_cb (r = 0.77), b_pushing_partner_cb and
##   b_pushing_self_cb (r = 0.89). This might lead to inappropriate results.
##   See 'Details' in '?rope'.

conditional_spaghetti(
  mood_gauss, 
  effects = c('pushing_partner_cw'),
  group_var = 'coupleID',
  plot_full_range = TRUE
)

$pushing_partner_cw

Reactance

range(df_double$reactance, na.rm = T) 
## [1] 0 5
hist(df_double$reactance, breaks = 7) 

hist(log(df_double$reactance+0.1), breaks = 10)

Ordinal

df_double$reactance_ordinal <- factor(df_double$reactance,
                                      levels = 0:5, 
                                      ordered = TRUE)

formula <- bf(
  reactance_ordinal ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID)
  
  , decomp = 'QR'
  #, autocor = autocor_str
)


prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = cumulative() # HURDLE_CUMULATIVE
#  )


#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

reactance_ordinal <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::cumulative(),
  #control = list(adapt_delta = 0.95),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777
  , file = file.path("models_cache_brms", paste0("reactance_ordinal", suffix))
)
## Warning: Rows containing NAs were excluded from the model.
reactance_ordinal_digest <- digest::digest(reactance_ordinal)
if (check_models) {
  check_brms(reactance_ordinal)
  DHARMa.check_brms.all(reactance_ordinal, outliers_type = 'bootstrap')
}
## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF    VIF 95% CI Increased SE Tolerance
##       pressure_self_cw 4.55 [4.23,  4.90]         2.13      0.22
##    pressure_partner_cw 1.54 [1.46,  1.63]         1.24      0.65
##        pushing_self_cw 1.29 [1.23,  1.36]         1.13      0.78
##     pushing_partner_cw 1.09 [1.05,  1.15]         1.04      0.92
##     persuasion_self_cb 1.08 [1.05,  1.15]         1.04      0.92
##  persuasion_partner_cb 1.07 [1.04,  1.14]         1.03      0.93
##       pressure_self_cb 1.39 [1.32,  1.47]         1.18      0.72
##    pressure_partner_cb 1.19 [1.14,  1.26]         1.09      0.84
##                    day 1.98 [1.86,  2.11]         1.41      0.51
##  Tolerance 95% CI
##      [0.20, 0.24]
##      [0.61, 0.69]
##      [0.74, 0.81]
##      [0.87, 0.95]
##      [0.87, 0.96]
##      [0.88, 0.97]
##      [0.68, 0.76]
##      [0.79, 0.88]
##      [0.47, 0.54]
## 
## Moderate Correlation
## 
##                Term  VIF    VIF 95% CI Increased SE Tolerance Tolerance 95% CI
##  persuasion_self_cw 8.66 [8.02,  9.37]         2.94      0.12     [0.11, 0.12]
##     pushing_self_cb 5.86 [5.44,  6.33]         2.42      0.17     [0.16, 0.18]
##  pushing_partner_cb 7.32 [6.78,  7.91]         2.70      0.14     [0.13, 0.15]
##           plan_self 5.46 [5.06,  5.89]         2.34      0.18     [0.17, 0.20]
##        plan_partner 6.22 [5.76,  6.72]         2.49      0.16     [0.15, 0.17]
## 
## High Correlation
## 
##                   Term   VIF    VIF 95% CI Increased SE Tolerance
##  persuasion_partner_cw 10.18 [9.41, 11.02]         3.19      0.10
##  Tolerance 95% CI
##      [0.09, 0.11]
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'x' in selecting a method for function 'print': Predictive errors are not defined for ordinal or categorical models.
## 
## Divergences:
## 3 of 40000 iterations ended with a divergence (0.0075%).
## Try increasing 'adapt_delta' to remove the divergences.
## 
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
## 
## Energy:
## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 6 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 2, observations = 756, p-value = 0.06
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.000000000 0.002017196
## sample estimates:
## outlier frequency (expected: 0.00041005291005291 ) 
##                                        0.002645503
if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(reactance_ordinal, variable = priorsense_vars)
}
summary_reactance_ordinal <- summarize_brms(
  reactance_ordinal, 
  stats_to_report = stats_to_report,
  rope_range = c(-0.18, 0.18),
  model_rows_fixed = model_rows_fixed_ordinal,
  model_rows_random = model_rows_random_ordinal,
  model_rownames_fixed = model_rownames_fixed_ordinal,
  model_rownames_random = model_rownames_random_ordinal,
  exponentiate = T) 
## Warning: There were 3 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning in compute_rope(range = rope_range): Collinearity detected. Some VIFs
## are > 10. This may invalidate ROPE inferences!

Check for Multicollinearity

Low Correlation

              Term  VIF    VIF 95% CI Increased SE Tolerance
  pressure_self_cw 4.55 [4.23,  4.90]         2.13      0.22

pressure_partner_cw 1.54 [1.46, 1.63] 1.24 0.65 pushing_self_cw 1.29 [1.23, 1.36] 1.13 0.78 pushing_partner_cw 1.09 [1.05, 1.15] 1.04 0.92 persuasion_self_cb 1.08 [1.05, 1.15] 1.04 0.92 persuasion_partner_cb 1.07 [1.04, 1.14] 1.03 0.93 pressure_self_cb 1.39 [1.32, 1.47] 1.18 0.72 pressure_partner_cb 1.19 [1.14, 1.26] 1.09 0.84 day 1.98 [1.86, 2.11] 1.41 0.51 Tolerance 95% CI [0.20, 0.24] [0.61, 0.69] [0.74, 0.81] [0.87, 0.95] [0.87, 0.96] [0.88, 0.97] [0.68, 0.76] [0.79, 0.88] [0.47, 0.54]

Moderate Correlation

           Term  VIF    VIF 95% CI Increased SE Tolerance Tolerance 95% CI

persuasion_self_cw 8.66 [8.02, 9.37] 2.94 0.12 [0.11, 0.12] pushing_self_cb 5.86 [5.44, 6.33] 2.42 0.17 [0.16, 0.18] pushing_partner_cb 7.32 [6.78, 7.91] 2.70 0.14 [0.13, 0.15] plan_self 5.46 [5.06, 5.89] 2.34 0.18 [0.17, 0.20] plan_partner 6.22 [5.76, 6.72] 2.49 0.16 [0.15, 0.17]

High Correlation

              Term   VIF    VIF 95% CI Increased SE Tolerance

persuasion_partner_cw 10.18 [9.41, 11.02] 3.19 0.10 Tolerance 95% CI [0.09, 0.11]

## Sampling priors, please wait...
summary_reactance_ordinal %>%
  print_df(rows_to_pack = rows_to_pack_ordinal)
OR SE 95% CI pd ROPE inside ROPE BF BF_Evidence Rhat Bulk_ESS Tail_ESS
Intercept NA NA NA NA NA NA NA NA NA NA NA
Intercept[1] 3.41*** 1.06 [ 1.88, 6.37] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.000 29678 29857
Intercept[2] 7.41*** 2.37 [ 4.00, 14.12] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.000 29774 29014
Intercept[3] 20.74*** 7.07 [ 10.82, 41.38] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.000 30595 29058
Intercept[4] 90.95*** 35.54 [ 43.19, 202.38] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.000 32305 29085
Intercept[5] 3085.94*** 2038.94 [928.01, 13025.16] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.000 41858 29149
Within-Person Effects
Daily individual’s experienced persuasion 0.84* 0.07 [ 0.71, 0.99] 0.980 [0.84, 1.20] 0.555 0.393 Weak Evidence for Null 1.000 35488 27412
Daily partner’s experienced persuasion 1.02 0.10 [ 0.83, 1.23] 0.585 [0.84, 1.20] 0.923 0.045 Strong Evidence for Null 1.000 32417 27395
Daily individual’s experienced pressure 1.84* 0.36 [ 1.16, 2.66] 0.992 [0.84, 1.20] 0.030 2.008 Weak Evidence 1.000 19813 21668
Daily partner’s experienced pressure 1.24 0.30 [ 0.69, 2.11] 0.806 [0.84, 1.20] 0.374 0.086 Strong Evidence for Null 1.000 20861 17900
Daily individual’s experienced pushing 1.21 0.13 [ 0.98, 1.53] 0.962 [0.84, 1.20] 0.449 0.213 Moderate Evidence for Null 1.000 28732 28538
Daily partner’s experienced pushing 0.94 0.12 [ 0.71, 1.23] 0.687 [0.84, 1.20] 0.773 0.050 Strong Evidence for Null 1.000 29004 22972
Day 1.49 0.52 [ 0.75, 2.95] 0.874 [0.84, 1.20] 0.214 0.074 Strong Evidence for Null 1.000 42893 30903
Own Actionplan 0.85 0.24 [ 0.48, 1.49] 0.718 [0.84, 1.20] 0.406 0.046 Strong Evidence for Null 1.000 42179 29531
Partner Actionplan 0.92 0.24 [ 0.56, 1.54] 0.619 [0.84, 1.20] 0.493 0.041 Strong Evidence for Null 1.000 43578 29710
Daily weartime NA NA NA NA NA NA NA NA NA NA NA
Between-Person Effects
Mean individual’s experienced persuasion 1.08 0.58 [ 0.36, 3.23] 0.560 [0.84, 1.20] 0.261 0.058 Strong Evidence for Null 1.000 19038 26032
Mean partner’s experienced persuasion 1.47 0.92 [ 0.45, 5.24] 0.736 [0.84, 1.20] 0.189 0.067 Strong Evidence for Null 1.000 20617 27126
Mean individual’s experienced pressure 3.74* 2.15 [ 1.23, 12.07] 0.989 [0.84, 1.20] 0.017 0.790 Weak Evidence for Null 1.000 17768 25613
Mean partner’s experienced pressure 1.14 0.69 [ 0.33, 3.70] 0.582 [0.84, 1.20] 0.224 0.052 Strong Evidence for Null 1.000 18385 24862
Mean individual’s experienced pushing 1.17 0.92 [ 0.25, 5.98] 0.580 [0.84, 1.20] 0.176 0.057 Strong Evidence for Null 1.000 15789 21244
Mean partner’s experienced pushing 0.08* 0.08 [ 0.01, 0.55] 0.994 [0.84, 1.20] 0.006 1.750 Weak Evidence 1.000 18548 24773
Mean weartime NA NA NA NA NA NA NA NA NA NA NA
Random Effects
sd(Intercept) 0.82 0.20 [0.48, 1.30] NA NA NA NA NA 1.000 14138 22006
sd(Daily individual’s experienced persuasion) 0.17 0.12 [0.01, 0.43] NA NA NA NA NA 1.000 7515 11752
sd(Daily partner’s experienced persuasion) 0.21 0.14 [0.01, 0.52] NA NA NA NA NA 1.000 8586 11983
sd(Daily individual’s experienced pressure) 0.56 0.25 [0.10, 1.18] NA NA NA NA NA 1.000 8771 6371
sd(Daily partner’s experienced pressure) 0.44 0.40 [0.02, 1.59] NA NA NA NA NA 1.000 8991 16845
sd(Daily individual’s experienced pushing) 0.21 0.14 [0.01, 0.51] NA NA NA NA NA 1.000 10556 12999
sd(Daily partner’s experienced pushing) 0.15 0.14 [0.01, 0.63] NA NA NA NA NA 1.000 14325 18401
Additional Parameters
sigma NA NA NA NA NA NA NA NA NA NA NA
disc 1.00 0.00 [1.00, 1.00] NA NA NA NA NA NA NA NA 
plot(
  bayestestR::p_direction(reactance_ordinal),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-6, 6)) +
  theme_bw()
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(reactance_ordinal, range = c(-0.18, 0.18), ci = 1)
) + theme_bw()
## Possible multicollinearity between b_Intercept[4] and b_Intercept[2] (r
##   = 0.81), b_Intercept[4] and b_Intercept[3] (r = 0.86),
##   b_pressure_self_cb and b_persuasion_self_cb (r = 0.73),
##   b_pressure_partner_cb and b_persuasion_partner_cb (r = 0.81). This might
##   lead to inappropriate results. See 'Details' in '?rope'.

conditional_spaghetti(
  reactance_ordinal, 
  effects = c('persuasion_self_cw', 'pressure_self_cw')
  , group_var = 'coupleID'
  #, n_groups = 15
  , plot_full_range = T
)

\(persuasion_self_cw <img src="01_FinalModelsPlan_files/figure-html/report_reactance_ordinal-3.png" width="2400" />\)pressure_self_cw

Binary

introduce_binary_reactance <- function(data) {
  data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
  return(data)
}



df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
  for (i in seq_along(implist)) {
    implist[[i]] <- introduce_binary_reactance(implist[[i]])
  }
}


formula <- bf(
  is_reactance ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID)
  
  , decomp = 'QR'
  #, autocor = autocor_str
  )



prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 10)", class = "Intercept", lb=0, ub=5) 
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = bernoulli()
#  )



#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

is_reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::bernoulli(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("is_reactance", suffix))
  #, file_refit = 'always'
)
## Warning: Rows containing NAs were excluded from the model.
is_reactance_digest <- digest::digest(is_reactance)
if (check_models) {
  check_brms(is_reactance)
  DHARMa.check_brms.all(is_reactance, integer = FALSE)
}
## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF   VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 1.06 [1.03, 1.13]         1.03      0.94
##  persuasion_partner_cw 1.15 [1.10, 1.22]         1.07      0.87
##       pressure_self_cw 1.05 [1.02, 1.12]         1.02      0.95
##    pressure_partner_cw 1.06 [1.02, 1.13]         1.03      0.95
##        pushing_self_cw 1.26 [1.20, 1.33]         1.12      0.79
##     pushing_partner_cw 1.15 [1.11, 1.22]         1.07      0.87
##     persuasion_self_cb 2.70 [2.52, 2.89]         1.64      0.37
##  persuasion_partner_cb 3.34 [3.12, 3.59]         1.83      0.30
##       pressure_self_cb 2.97 [2.78, 3.19]         1.72      0.34
##    pressure_partner_cb 3.14 [2.93, 3.37]         1.77      0.32
##        pushing_self_cb 2.20 [2.06, 2.35]         1.48      0.46
##     pushing_partner_cb 2.05 [1.93, 2.19]         1.43      0.49
##              plan_self 1.67 [1.58, 1.78]         1.29      0.60
##           plan_partner 1.55 [1.46, 1.64]         1.24      0.65
##                    day 1.06 [1.03, 1.13]         1.03      0.95
##  Tolerance 95% CI
##      [0.88, 0.97]
##      [0.82, 0.91]
##      [0.89, 0.98]
##      [0.89, 0.98]
##      [0.75, 0.83]
##      [0.82, 0.90]
##      [0.35, 0.40]
##      [0.28, 0.32]
##      [0.31, 0.36]
##      [0.30, 0.34]
##      [0.43, 0.48]
##      [0.46, 0.52]
##      [0.56, 0.63]
##      [0.61, 0.68]
##      [0.89, 0.98]
## # Distribution of Model Family
## 
## Predicted Distribution of Residuals
## 
##  Distribution Probability
##       tweedie         34%
##        normal         19%
##     bernoulli          9%
## 
## Predicted Distribution of Response
## 
##   Distribution Probability
##      bernoulli         81%
##  beta-binomial         12%
##       binomial          6%
## 
## Divergences:
## 0 of 40000 iterations ended with a divergence.
## 
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
## 
## Energy:
## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 32 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.

## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  model.check
## outliers at both margin(s) = 0, observations = 756, p-value = 0.4141
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
##  0.000000000 0.004867585
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 ) 
##                                                    0
if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(is_reactance, variable = priorsense_vars)
}
summary_is_reactance <- summarize_brms(
  is_reactance, 
  stats_to_report = stats_to_report,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) 
## Sampling priors, please wait...
summary_is_reactance %>%
  print_df(rows_to_pack = rows_to_pack)
OR SE 95% CI pd ROPE inside ROPE BF BF_Evidence Rhat Bulk_ESS Tail_ESS
Intercept 0.34** 0.11 [0.17, 0.65] 0.999 [0.83, 1.20] 0.004 9.883 Moderate Evidence 1.000 43983 32996
Within-Person Effects
Daily individual’s experienced persuasion 0.84 0.08 [0.68, 1.01] 0.968 [0.83, 1.20] 0.524 0.289 Moderate Evidence for Null 1.000 39116 35830
Daily partner’s experienced persuasion 1.12 0.16 [0.84, 1.53] 0.779 [0.83, 1.20] 0.663 0.088 Strong Evidence for Null 1.000 30621 31810
Daily individual’s experienced pressure 1.98* 0.64 [1.02, 4.58] 0.978 [0.83, 1.20] 0.053 0.946 Weak Evidence for Null 1.000 27810 23603
Daily partner’s experienced pressure 1.41 0.62 [0.54, 4.37] 0.795 [0.83, 1.20] 0.231 0.139 Moderate Evidence for Null 1.000 27990 25348
Daily individual’s experienced pushing 1.34* 0.18 [1.04, 1.76] 0.988 [0.83, 1.20] 0.206 0.649 Weak Evidence for Null 1.000 39055 33035
Daily partner’s experienced pushing 0.92 0.18 [0.61, 1.37] 0.671 [0.83, 1.20] 0.601 0.073 Strong Evidence for Null 1.000 40541 30682
Day 1.68 0.67 [0.77, 3.69] 0.904 [0.83, 1.20] 0.161 0.101 Moderate Evidence for Null 1.000 64852 30859
Own Actionplan 0.87 0.28 [0.46, 1.62] 0.669 [0.83, 1.20] 0.392 0.048 Strong Evidence for Null 1.000 68276 30117
Partner Actionplan 0.88 0.26 [0.49, 1.58] 0.669 [0.83, 1.20] 0.425 0.048 Strong Evidence for Null 1.000 73521 28927
Daily weartime NA NA NA NA NA NA NA NA NA NA NA
Between-Person Effects
Mean individual’s experienced persuasion 1.89 1.26 [0.51, 7.72] 0.832 [0.83, 1.20] 0.135 0.112 Moderate Evidence for Null 1.000 29689 32175
Mean partner’s experienced persuasion 1.99 1.51 [0.47, 9.47] 0.825 [0.83, 1.20] 0.129 0.101 Moderate Evidence for Null 1.000 29786 32205
Mean individual’s experienced pressure 26.35** 32.93 [2.64, 404.79] 0.998 [0.83, 1.20] 0.002 6.651 Moderate Evidence 1.000 23979 25932
Mean partner’s experienced pressure 1.77 2.30 [0.11, 19.86] 0.669 [0.83, 1.20] 0.095 0.124 Moderate Evidence for Null 1.000 22964 28932
Mean individual’s experienced pushing 0.61 0.69 [0.06, 5.70] 0.672 [0.83, 1.20] 0.115 0.089 Strong Evidence for Null 1.000 23562 29427
Mean partner’s experienced pushing 0.05* 0.06 [0.00, 0.55] 0.992 [0.83, 1.20] 0.007 1.550 Weak Evidence 1.000 23173 30242
Mean weartime NA NA NA NA NA NA NA NA NA NA NA
Random Effects
sd(Intercept) 1.18 0.26 [0.75, 1.79] NA NA NA NA NA 1.000 17774 26197
sd(Daily individual’s experienced persuasion) 0.21 0.14 [0.01, 0.51] NA NA NA NA NA 1.000 10723 17026
sd(Daily partner’s experienced persuasion) 0.49 0.21 [0.12, 0.99] NA NA NA NA NA 1.001 13264 12658
sd(Daily individual’s experienced pressure) 1.10 0.56 [0.13, 2.47] NA NA NA NA NA 1.001 9043 8898
sd(Daily partner’s experienced pressure) 0.86 0.70 [0.05, 2.83] NA NA NA NA NA 1.000 15190 21082
sd(Daily individual’s experienced pushing) 0.25 0.16 [0.02, 0.61] NA NA NA NA NA 1.000 14769 18126
sd(Daily partner’s experienced pushing) 0.27 0.24 [0.01, 0.99] NA NA NA NA NA 1.000 16875 20309
Additional Parameters
sigma NA NA NA NA NA NA NA NA NA NA NA 
plot(
  bayestestR::p_direction(is_reactance),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-6, 6)) +
  theme_bw()
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(is_reactance, ci = 1)
) + theme_bw()

conditional_spaghetti(
  is_reactance, 
  effects = c('pressure_self_cw', 'pushing_self_cw'),
  group_var = 'coupleID',
  plot_full_range = TRUE
)

\(pressure_self_cw <img src="01_FinalModelsPlan_files/figure-html/report_is_reactance-3.png" width="2400" />\)pushing_self_cw 

hypothesis(is_reactance, "exp(pressure_self_cw) > exp(pushing_self_cw)")
## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (exp(pressure_sel... > 0     0.83      1.01     -0.3     2.56       6.26
##   Post.Prob Star
## 1      0.86     
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.

Report All Models

all_models <- cbind(
  summary_pa_sub[, !grepl('Rhat|ESS', colnames(summary_pa_sub))],
  summary_pa_obj[, !grepl('Rhat|ESS', colnames(summary_pa_obj))],
  summary_mood[, !grepl('Rhat|ESS', colnames(summary_mood))], 
  summary_is_reactance[, !grepl('Rhat|ESS', colnames(summary_is_reactance))]
  #, summary_reactance_ordinal[, !grepl('Rhat|ESS', colnames(summary_reactance_ordinal))]
)



# pretty printing
if (get_bayesfactor) {
  summary_all_models <- all_models %>%
  print_df(rows_to_pack = rows_to_pack) %>%
  add_header_above(
    c(" ", "Subjective MVPA Hurdle Lognormal" = 16,  
      "Device-Based MVPA Log (Gaussian)" = 8, 
      "Mood Gaussian" = 8,
      #"Reactance Ordinal" = 8,
      "Reactance Dichotome" = 8
  )
)
} else {
  summary_all_models <- all_models %>%
    print_df(rows_to_pack = rows_to_pack) %>%
    add_header_above(
      c(" ", "Subjective MVPA Hurdle Lognormal" = 12,  
        "Device-Based MVPA Log (Gaussian)" = 6, 
        "Mood Gaussian" = 6,
        #"Reactance Ordinal" = 6,
        "Reactance Dichotome" = 6
    )
  )
}

export_xlsx(
  summary_all_models, 
  rows_to_pack = rows_to_pack,
  file.path("Output", paste0("AllModels", suffix, ".xlsx")), 
  merge_option = 'header', 
  simplify_2nd_row = TRUE,
  line_above_rows = c(1,2),
  line_below_rows = c(-1)
)
## 
## Attaching package: 'rvest'
## The following object is masked from 'package:readr':
## 
##     guess_encoding
summary_all_models
Subjective MVPA Hurdle Lognormal
Device-Based MVPA Log (Gaussian)
Mood Gaussian
Reactance Dichotome
exp(Est.)_hu SE_hu 95% CI_hu pd_hu ROPE_hu inside ROPE_hu BF_hu BF_Evidence_hu exp(Est.)_nonzero SE_nonzero 95% CI_nonzero pd_nonzero ROPE_nonzero inside ROPE_nonzero BF_nonzero BF_Evidence_nonzero exp(Est.) SE 95% CI pd ROPE inside ROPE BF BF_Evidence Est. SE 95% CI pd ROPE inside ROPE BF BF_Evidence OR SE 95% CI pd ROPE inside ROPE BF BF_Evidence
Intercept 0.26*** 0.04 [ 0.19, 0.35] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 37.84*** 2.60 [33.00, 43.33] 1.000 [0.92, 1.08] 0.000 >100 Overwhelming Evidence 111.19*** 6.10 [99.90, 123.80] 1.000 [0.94, 1.07] 0.000 >100 Overwhelming Evidence 3.67*** 0.11 [ 3.46, 3.88] 1.000 [-0.11, 0.11] 0.000 >100 Overwhelming Evidence 0.34** 0.11 [0.17, 0.65] 0.999 [0.83, 1.20] 0.004 9.883 Moderate Evidence
Within-Person Effects
Daily individual’s experienced persuasion 1.58*** 0.11 [ 1.39, 1.83] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.03 0.03 [ 0.98, 1.09] 0.868 [0.92, 1.08] 0.963 0.017 Very Strong Evidence for Null 1.03 0.02 [ 1.00, 1.06] 0.954 [0.94, 1.07] 0.995 0.023 Very Strong Evidence for Null 0.00 0.02 [-0.04, 0.04] 0.521 [-0.11, 0.11] 1.000 0.004 Very Strong Evidence for Null 0.84 0.08 [0.68, 1.01] 0.968 [0.83, 1.20] 0.524 0.289 Moderate Evidence for Null
Daily partner’s experienced persuasion 1.34*** 0.08 [ 1.19, 1.52] 1.000 [0.84, 1.20] 0.035 >100 Overwhelming Evidence 1.03 0.02 [ 0.99, 1.08] 0.928 [0.92, 1.08] 0.974 0.023 Very Strong Evidence for Null 1.02 0.02 [ 0.99, 1.05] 0.866 [0.94, 1.07] 0.998 0.010 Very Strong Evidence for Null 0.02 0.02 [-0.02, 0.07] 0.813 [-0.11, 0.11] 1.000 0.006 Very Strong Evidence for Null 1.12 0.16 [0.84, 1.53] 0.779 [0.83, 1.20] 0.663 0.088 Strong Evidence for Null
Daily individual’s experienced pressure 0.96 0.15 [ 0.69, 1.30] 0.614 [0.84, 1.20] 0.739 0.079 Strong Evidence for Null 0.91 0.05 [ 0.82, 1.00] 0.972 [0.92, 1.08] 0.345 0.050 Strong Evidence for Null 0.95 0.03 [ 0.88, 1.01] 0.950 [0.94, 1.07] 0.599 0.019 Very Strong Evidence for Null -0.03 0.05 [-0.14, 0.07] 0.716 [-0.11, 0.11] 0.936 0.004 Very Strong Evidence for Null 1.98* 0.64 [1.02, 4.58] 0.978 [0.83, 1.20] 0.053 0.946 Weak Evidence for Null
Daily partner’s experienced pressure 1.49* 0.28 [ 1.05, 2.34] 0.986 [0.84, 1.20] 0.111 1.185 Weak Evidence 0.95 0.04 [ 0.86, 1.03] 0.892 [0.92, 1.08] 0.700 0.014 Very Strong Evidence for Null 0.98 0.03 [ 0.92, 1.05] 0.692 [0.94, 1.07] 0.911 0.005 Very Strong Evidence for Null -0.03 0.05 [-0.15, 0.08] 0.691 [-0.11, 0.11] 0.927 0.004 Very Strong Evidence for Null 1.41 0.62 [0.54, 4.37] 0.795 [0.83, 1.20] 0.231 0.139 Moderate Evidence for Null
Daily individual’s experienced pushing 0.95 0.14 [ 0.71, 1.30] 0.636 [0.84, 1.20] 0.740 0.081 Strong Evidence for Null 0.99 0.03 [ 0.92, 1.05] 0.640 [0.92, 1.08] 0.969 0.008 Very Strong Evidence for Null 1.01 0.02 [ 0.96, 1.07] 0.730 [0.94, 1.07] 0.976 0.007 Very Strong Evidence for Null 0.01 0.03 [-0.06, 0.07] 0.575 [-0.11, 0.11] 0.999 0.004 Very Strong Evidence for Null 1.34* 0.18 [1.04, 1.76] 0.988 [0.83, 1.20] 0.206 0.649 Weak Evidence for Null
Daily partner’s experienced pushing 1.29* 0.14 [ 1.05, 1.62] 0.993 [0.84, 1.20] 0.247 1.030 Weak Evidence 0.96 0.03 [ 0.90, 1.02] 0.916 [0.92, 1.08] 0.878 0.018 Very Strong Evidence for Null 1.00 0.02 [ 0.96, 1.04] 0.517 [0.94, 1.07] 0.997 0.005 Very Strong Evidence for Null 0.07* 0.03 [ 0.00, 0.14] 0.977 [-0.11, 0.11] 0.896 0.034 Strong Evidence for Null 0.92 0.18 [0.61, 1.37] 0.671 [0.83, 1.20] 0.601 0.073 Strong Evidence for Null
Day 0.86 0.13 [ 0.64, 1.16] 0.834 [0.84, 1.20] 0.576 0.119 Moderate Evidence for Null 0.99 0.06 [ 0.88, 1.12] 0.577 [0.92, 1.08] 0.786 0.007 Very Strong Evidence for Null 0.97 0.03 [ 0.91, 1.04] 0.786 [0.94, 1.07] 0.842 0.006 Very Strong Evidence for Null 0.26*** 0.06 [ 0.15, 0.37] 1.000 [-0.11, 0.11] 0.006 62.833 Very Strong Evidence 1.68 0.67 [0.77, 3.69] 0.904 [0.83, 1.20] 0.161 0.101 Moderate Evidence for Null
Own Actionplan 9.45*** 0.96 [ 7.76, 11.57] 1.000 [0.84, 1.20] 0.000 >100 Overwhelming Evidence 1.32*** 0.06 [ 1.21, 1.45] 1.000 [0.92, 1.08] 0.000 >100 Overwhelming Evidence 1.06* 0.03 [ 1.01, 1.12] 0.994 [0.94, 1.07] 0.527 0.103 Moderate Evidence for Null 0.10** 0.04 [ 0.03, 0.18] 0.996 [-0.11, 0.11] 0.619 0.106 Moderate Evidence for Null 0.87 0.28 [0.46, 1.62] 0.669 [0.83, 1.20] 0.392 0.048 Strong Evidence for Null
Partner Actionplan 1.17 0.12 [ 0.96, 1.42] 0.940 [0.84, 1.20] 0.601 0.168 Moderate Evidence for Null 1.08 0.05 [ 0.99, 1.17] 0.956 [0.92, 1.08] 0.541 0.032 Strong Evidence for Null 1.05 0.03 [ 1.00, 1.10] 0.972 [0.94, 1.07] 0.751 0.025 Very Strong Evidence for Null -0.03 0.04 [-0.11, 0.04] 0.791 [-0.11, 0.11] 0.983 0.004 Very Strong Evidence for Null 0.88 0.26 [0.49, 1.58] 0.669 [0.83, 1.20] 0.425 0.048 Strong Evidence for Null
Daily weartime NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1.00*** 0.00 [ 1.00, 1.00] 1.000 [0.94, 1.07] 1.000 15.446 Strong Evidence NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Between-Person Effects
Mean individual’s experienced persuasion 1.49 0.54 [ 0.72, 3.06] 0.863 [0.84, 1.20] 0.216 0.331 Weak Evidence for Null 0.99 0.15 [ 0.74, 1.34] 0.514 [0.92, 1.08] 0.394 0.015 Very Strong Evidence for Null 1.10 0.15 [ 0.84, 1.46] 0.761 [0.94, 1.07] 0.281 0.016 Very Strong Evidence for Null 0.34 0.28 [-0.23, 0.90] 0.887 [-0.11, 0.11] 0.150 0.028 Very Strong Evidence for Null 1.89 1.26 [0.51, 7.72] 0.832 [0.83, 1.20] 0.135 0.112 Moderate Evidence for Null
Mean partner’s experienced persuasion 1.40 0.50 [ 0.69, 2.85] 0.826 [0.84, 1.20] 0.252 0.280 Moderate Evidence for Null 0.95 0.14 [ 0.71, 1.28] 0.636 [0.92, 1.08] 0.371 0.015 Very Strong Evidence for Null 0.98 0.14 [ 0.74, 1.30] 0.560 [0.94, 1.07] 0.352 0.013 Very Strong Evidence for Null 0.23 0.28 [-0.34, 0.79] 0.791 [-0.11, 0.11] 0.231 0.019 Very Strong Evidence for Null 1.99 1.51 [0.47, 9.47] 0.825 [0.83, 1.20] 0.129 0.101 Moderate Evidence for Null
Mean individual’s experienced pressure 0.39* 0.16 [ 0.17, 0.88] 0.988 [0.84, 1.20] 0.030 2.722 Weak Evidence 1.21 0.21 [ 0.86, 1.69] 0.861 [0.92, 1.08] 0.203 0.019 Very Strong Evidence for Null 0.99 0.14 [ 0.74, 1.32] 0.530 [0.94, 1.07] 0.345 0.007 Very Strong Evidence for Null -0.30 0.27 [-0.85, 0.25] 0.863 [-0.11, 0.11] 0.182 0.016 Very Strong Evidence for Null 26.35** 32.93 [2.64, 404.79] 0.998 [0.83, 1.20] 0.002 6.651 Moderate Evidence
Mean partner’s experienced pressure 0.49 0.21 [ 0.21, 1.14] 0.951 [0.84, 1.20] 0.087 0.844 Weak Evidence for Null 0.94 0.17 [ 0.66, 1.33] 0.638 [0.92, 1.08] 0.318 0.011 Very Strong Evidence for Null 0.97 0.14 [ 0.74, 1.29] 0.577 [0.94, 1.07] 0.352 0.008 Very Strong Evidence for Null -0.32 0.27 [-0.87, 0.23] 0.876 [-0.11, 0.11] 0.172 0.017 Very Strong Evidence for Null 1.77 2.30 [0.11, 19.86] 0.669 [0.83, 1.20] 0.095 0.124 Moderate Evidence for Null
Mean individual’s experienced pushing 1.10 0.59 [ 0.38, 3.17] 0.573 [0.84, 1.20] 0.261 0.275 Moderate Evidence for Null 1.24 0.28 [ 0.80, 1.94] 0.838 [0.92, 1.08] 0.174 0.020 Very Strong Evidence for Null 0.95 0.19 [ 0.64, 1.44] 0.600 [0.94, 1.07] 0.246 0.011 Very Strong Evidence for Null 0.20 0.39 [-0.57, 0.99] 0.699 [-0.11, 0.11] 0.206 0.012 Very Strong Evidence for Null 0.61 0.69 [0.06, 5.70] 0.672 [0.83, 1.20] 0.115 0.089 Strong Evidence for Null
Mean partner’s experienced pushing 1.96 1.04 [ 0.69, 5.73] 0.899 [0.84, 1.20] 0.120 0.591 Weak Evidence for Null 1.34 0.31 [ 0.85, 2.11] 0.903 [0.92, 1.08] 0.118 0.031 Strong Evidence for Null 1.22 0.24 [ 0.82, 1.83] 0.842 [0.94, 1.07] 0.154 0.018 Very Strong Evidence for Null 0.36 0.39 [-0.41, 1.15] 0.827 [-0.11, 0.11] 0.150 0.017 Very Strong Evidence for Null 0.05* 0.06 [0.00, 0.55] 0.992 [0.83, 1.20] 0.007 1.550 Weak Evidence
Mean weartime NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1.00 0.00 [ 1.00, 1.00] 0.916 [0.94, 1.07] 1.000 0.017 Very Strong Evidence for Null NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Random Effects
sd(Intercept) 0.74 0.10 [0.57, 0.99] NA NA NA NA NA 0.30 0.04 [0.23, 0.40] NA NA NA NA NA 0.29 0.04 [0.23, 0.39] NA NA NA NA NA 0.60 0.07 [0.47, 0.78] NA NA NA NA NA 1.18 0.26 [0.75, 1.79] NA NA NA NA NA
sd(Daily individual’s experienced persuasion) 0.21 0.09 [0.03, 0.39] NA NA NA NA NA 0.11 0.02 [0.07, 0.17] NA NA NA NA NA 0.05 0.01 [0.02, 0.08] NA NA NA NA NA 0.04 0.03 [0.00, 0.10] NA NA NA NA NA 0.21 0.14 [0.01, 0.51] NA NA NA NA NA
sd(Daily partner’s experienced persuasion) 0.15 0.09 [0.01, 0.34] NA NA NA NA NA 0.08 0.02 [0.04, 0.13] NA NA NA NA NA 0.05 0.02 [0.02, 0.09] NA NA NA NA NA 0.07 0.03 [0.01, 0.13] NA NA NA NA NA 0.49 0.21 [0.12, 0.99] NA NA NA NA NA
sd(Daily individual’s experienced pressure) 0.20 0.18 [0.01, 0.74] NA NA NA NA NA 0.07 0.06 [0.00, 0.23] NA NA NA NA NA 0.04 0.03 [0.00, 0.14] NA NA NA NA NA 0.07 0.06 [0.00, 0.23] NA NA NA NA NA 1.10 0.56 [0.13, 2.47] NA NA NA NA NA
sd(Daily partner’s experienced pressure) 0.24 0.22 [0.01, 0.94] NA NA NA NA NA 0.06 0.05 [0.00, 0.18] NA NA NA NA NA 0.03 0.03 [0.00, 0.11] NA NA NA NA NA 0.08 0.07 [0.00, 0.26] NA NA NA NA NA 0.86 0.70 [0.05, 2.83] NA NA NA NA NA
sd(Daily individual’s experienced pushing) 0.59 0.17 [0.31, 1.01] NA NA NA NA NA 0.09 0.04 [0.01, 0.17] NA NA NA NA NA 0.07 0.04 [0.01, 0.15] NA NA NA NA NA 0.05 0.04 [0.00, 0.14] NA NA NA NA NA 0.25 0.16 [0.02, 0.61] NA NA NA NA NA
sd(Daily partner’s experienced pushing) 0.18 0.14 [0.01, 0.50] NA NA NA NA NA 0.07 0.04 [0.01, 0.15] NA NA NA NA NA 0.03 0.03 [0.00, 0.10] NA NA NA NA NA 0.07 0.05 [0.01, 0.16] NA NA NA NA NA 0.27 0.24 [0.01, 0.99] NA NA NA NA NA
Additional Parameters
sigma NA NA NA NA NA NA NA NA 0.68 0.01 [0.65, 0.70] NA NA NA NA NA 0.57 0.01 [0.56, 0.59] NA NA NA NA NA 0.96 0.01 [0.94, 0.98] NA NA NA NA NA NA NA NA NA NA NA NA NA 
report::report_system()

Analyses were conducted using the R Statistical language (version 4.4.2; R Core Team, 2024) on Windows 11 x64 (build 26100)

report::report_packages()
  • beepr (version 2.0; Bååth R, 2024)
  • R.methodsS3 (version 1.8.2; Bengtsson H, 2003)
  • R.oo (version 1.27.0; Bengtsson H, 2003)
  • R.utils (version 2.12.3; Bengtsson H, 2023)
  • brms (version 2.22.0; Bürkner P, 2017)
  • digest (version 0.6.37; Eddelbuettel D, 2024)
  • Rcpp (version 1.0.13.1; Eddelbuettel D et al., 2024)
  • bayesplot (version 1.11.1; Gabry J, Mahr T, 2024)
  • lubridate (version 1.9.3; Grolemund G, Wickham H, 2011)
  • DHARMa (version 0.4.7; Hartig F, 2024)
  • wbCorr (version 0.1.22; Küng P, 2023)
  • see (version 0.9.0; Lüdecke D et al., 2021)
  • tibble (version 3.2.1; Müller K, Wickham H, 2023)
  • R (version 4.4.2; R Core Team, 2024)
  • openxlsx (version 4.2.7.1; Schauberger P, Walker A, 2024)
  • ggplot2 (version 3.5.1; Wickham H, 2016)
  • forcats (version 1.0.0; Wickham H, 2023)
  • stringr (version 1.5.1; Wickham H, 2023)
  • rvest (version 1.0.4; Wickham H, 2024)
  • tidyverse (version 2.0.0; Wickham H et al., 2019)
  • readxl (version 1.4.3; Wickham H, Bryan J, 2023)
  • dplyr (version 1.1.4; Wickham H et al., 2023)
  • purrr (version 1.0.2; Wickham H, Henry L, 2023)
  • readr (version 2.1.5; Wickham H et al., 2024)
  • xml2 (version 1.3.6; Wickham H et al., 2023)
  • tidyr (version 1.3.1; Wickham H et al., 2024)
  • knitr (version 1.49; Xie Y, 2024)
  • kableExtra (version 1.4.0; Zhu H, 2024)
report::cite_packages()